Peter Mburu
03/04/2024, 12:08 PMScout
03/04/2024, 12:08 PMOutput
event in Dagster is a critical component for passing data between steps in a pipeline. When you define a computation or a step within a pipeline using the @op
decorator, you can use the Output
object to send the result of that computation to subsequent steps. Here's how you can use `Output`: 1. Returning Output Directly: For many use cases, you can return a value directly from an op, and Dagster will handle it as an output. This is the simplest form of creating an output. python @op def my_output_op(): return 5
2. Using Output Object: If you need additional functionality, such as specifying output metadata or conditional branching, you can use the Output
object. This object can be either returned or yielded from an op, and it allows you to attach metadata to the output or to specify the output's name if there are multiple outputs. python from dagster import Output, op @op def my_output_op() -> Output: return Output("some_value", metadata={"some_metadata": "a_value"})
3. Parameterized Type Annotations: When using the Output
object, you can also provide type annotations to ensure type safety and clarity in your pipeline's design. python from dagster import Output, op @op def my_output_generic_op() -> Output[int]: return Output(5, metadata={"some_metadata": "a_value"})
4. Yielding Multiple Outputs: If an op has multiple outputs, you can yield Output
objects with specified output names. When yielding outputs, type annotations cannot be used directly, but you can specify type information using the out
argument of the op decorator. python from dagster import Output, op, Out @op(out={"out1": Out(str), "out2": Out(int)}) def my_op_yields(): yield Output("foo", output_name="out1") yield Output(5, output_name="out2")
5. Attaching Metadata to Outputs: You can attach metadata to an output to log information specific to that output. This is done using the metadata
parameter of the Output
object. The Output
event is a flexible and powerful way to manage data flow and metadata within Dagster pipelines, providing clear interfaces for single and multiple outputs, type safety, and additional metadata capabilities.Peter Mburu
03/04/2024, 12:19 PMScout
03/04/2024, 12:19 PMpartition_key
property within a non-partitioned run. The partition_key
is only relevant when you are working with partitioned assets or runs, which are a way to slice your data processing into discrete chunks that can be processed independently, often corresponding to logical timeframes like days or months. Here's a breakdown of the error message: 1. The partition_key
property is being accessed within the remote_imf_data
function. 2. The partition_key
is being accessed through the context
object, which is provided to the function during execution. 3. The error is raised because the current run is not partitioned, and therefore, the partition_key
property is not set and should not be accessed. To resolve this issue, you have a few options: - If the run is supposed to be partitioned, ensure that you are setting up the partitions correctly in your asset or pipeline definition and that you are initiating a partitioned run. - If the run is not supposed to be partitioned, you should modify the remote_imf_data
function to avoid accessing the partition_key
property, or handle the case where it is not available. Here's an example of how you might modify the remote_imf_data
function to handle the absence of a `partition_key`: python @op def remote_imf_data(context, metadata_df, document_type, indicators, out_name): if context.has_partition_key: period_date = context.partition_key # ... use period_date in your logic else: # ... handle the non-partitioned case
In this example, context.has_partition_key
is a made-up property for illustration purposes. You would need to implement a check to determine whether the partition_key
is available in your specific context. Remember to review your pipeline or asset definitions to ensure that partitioning is set up correctly if it is required for your use case. If you continue to face issues, you may want to look into the documentation or source code related to partitioning in Dagster or seek help from the community or support channels.Peter Mburu
03/04/2024, 12:22 PMScout
03/04/2024, 12:22 PMmaterialize_to_memory
for testing partitioned assets in Dagster, you can pass the partition_key
as an argument to specify which partition you want to materialize. This is useful when you want to test the behavior of assets under different partitioned conditions. Here's an example of how you might use materialize_to_memory
with a `partition_key`: python from dagster import materialize_to_memory result = materialize_to_memory( assets=[your_asset], partition_key="2023-01-01" # Replace with the appropriate partition key )
In this example, your_asset
is the asset you want to test, and "2023-01-01"
is the partition key that corresponds to the partition you want to materialize. Replace "2023-01-01"
with the actual partition key that is relevant to your test. If you encounter issues with passing the partition_key
, ensure that: 1. The asset you are testing is indeed set up with a partitioning scheme. 2. The partition_key
you are passing matches one of the defined partitions for the asset. 3. The asset's dependencies, if any, are also compatible with the specified partition_key
. If you continue to face issues, it may be helpful to refer to the documentation for the most up-to-date usage patterns and examples related to partitioned assets and testing with materialize_to_memory
. Additionally, you can check for any relevant discussions or issues in the Dagster community that might address similar testing scenarios.Peter Mburu
03/04/2024, 12:24 PMScout
03/04/2024, 12:24 PMmaterialize
function during testing. This will execute the assets and allow you to inspect the results. Here's a step-by-step guide on how to test multiple assets: 1. Import the assets: Import all the assets you want to test into your test file. 2. Group the assets: Create a list that contains all the assets you want to test together. 3. Materialize the assets: Use the materialize
function to execute the assets. This function returns an ExecuteInProcessResult
object. 4. Assert the results: Use the ExecuteInProcessResult
object to check the success of the execution and to retrieve the outputs of the assets for further assertions. Here's an example of how you might write a test for a group of assets: python from dagster import materialize from your_project.assets import asset1, asset2, asset3 def test_assets(): assets = [asset1, asset2, asset3] result = materialize(assets) assert result.success # If you want to assert on the output of specific assets, you can do so like this: output_asset1 = result.output_for_node("asset1") output_asset2 = result.output_for_node("asset2") # ... and so on for each asset you want to check. # Perform your assertions on the outputs assert output_asset1 is not None assert output_asset2 == expected_value # ... and so on for each asset you want to check.
In this example, asset1
, asset2
, and asset3
are the assets you are testing. The output_for_node
method is used to retrieve the output of each asset by its name. You can then perform assertions on these outputs to ensure they meet your expectations. Remember to replace your_project.assets
with the actual module path where your assets are defined, and asset1
, asset2
, asset3
with the actual names of your assets. To run the tests, you can use a test runner like pytest
. Navigate to the directory containing your test file and run the test runner command (e.g., pytest test_assets.py
). Always refer to the documentation for the latest best practices and examples on testing assets in Dagster.Peter Mburu
03/04/2024, 12:42 PMScout
03/04/2024, 12:42 PMmulti_asset
in Dagster, which is a function that can produce multiple assets as outputs. Let's break down the code: 1. Imports: The code imports AssetOut
, Output
, and multi_asset
from the dagster
module. These are used to define the outputs of the multi-asset and to yield values for those outputs. 2. Multi-Asset Definition: The @multi_asset
decorator is used to define a multi-asset function named split_actions
. This function can produce two separate assets, named "a" and "b". 3. Outputs Specification: - The outs
parameter of the @multi_asset
decorator is a dictionary that defines the outputs of the multi-asset. Each key in the dictionary corresponds to an output name, and the value is an AssetOut
object. - AssetOut(is_required=False)
indicates that the corresponding output asset is optional. This means that the multi-asset function can complete successfully without necessarily yielding a value for these outputs. 4. Subsetting: The can_subset=True
parameter allows for subsetting, which means that when this multi-asset is executed, it can produce a subset of its defined outputs based on the selection criteria provided at runtime. 5. Multi-Asset Function: - The split_actions
function takes an AssetExecutionContext
object as its context parameter. This context provides system information such as resources, config, and logging, and it is specific to asset execution. - Inside the function, there are conditional checks to determine which outputs have been selected for execution using context.op_execution_context.selected_output_names
. This attribute contains the names of the outputs that have been selected for materialization. - If "a" is in the selected output names, the function yields an Output
with the value 123
and the output_name
"a". Similarly, if "b" is in the selected output names, it yields an Output
with the value 456
and the output_name
"b". The yield
statements are used to emit the values for each asset. When the function is executed, it will check which outputs have been selected and only produce those that are required. This allows for flexibility in asset generation and can be particularly useful when different outputs are needed under different circumstances or based on different configurations. In summary, this code defines a multi-asset in Dagster that can optionally produce two separate assets, "a" and "b", based on the outputs selected at runtime. The values of these assets are 123
and 456
, respectively.Peter Mburu
03/04/2024, 12:46 PMScout
03/04/2024, 12:46 PMselected_output_names
to conditionally yield outputs. Each multi-asset will have two potential outputs, "output1" and "output2", and the yielding of these outputs will depend on whether they have been selected for execution. python from dagster import AssetOut, Output, multi_asset, AssetExecutionContext # First multi-asset @multi_asset( outs={ "output1": AssetOut(is_required=False), "output2": AssetOut(is_required=False), }, can_subset=True, ) def multi_asset_one(context: AssetExecutionContext): if "output1" in context.op_execution_context.selected_output_names: yield Output(value="value1_asset_one", output_name="output1") if "output2" in context.op_execution_context.selected_output_names: yield Output(value="value2_asset_one", output_name="output2") # Second multi-asset @multi_asset( outs={ "output1": AssetOut(is_required=False), "output2": AssetOut(is_required=False), }, can_subset=True, ) def multi_asset_two(context: AssetExecutionContext): if "output1" in context.op_execution_context.selected_output_names: yield Output(value="value1_asset_two", output_name="output1") if "output2" in context.op_execution_context.selected_output_names: yield Output(value="value2_asset_two", output_name="output2") # Third multi-asset @multi_asset( outs={ "output1": AssetOut(is_required=False), "output2": AssetOut(is_required=False), }, can_subset=True, ) def multi_asset_three(context: AssetExecutionContext): if "output1" in context.op_execution_context.selected_output_names: yield Output(value="value1_asset_three", output_name="output1") if "output2" in context.op_execution_context.selected_output_names: yield Output(value="value2_asset_three", output_name="output2")
In this example, each multi-asset function checks the selected_output_names
to determine which outputs have been selected for execution. If an output name is present in the selected_output_names
, the function yields an Output
with a corresponding value and output name. When you execute these multi-assets, you can specify which outputs you want to materialize by providing the selected output names. This allows you to subset the outputs and only materialize the ones you need. The can_subset
parameter is set to True
to enable this behavior. Please note that the actual values and output names are placeholders and should be replaced with values that make sense for your specific use case.Peter Mburu
03/04/2024, 12:48 PMScout
03/04/2024, 12:48 PMAssetIn
and AssetOut
classes to define the inputs and outputs of each asset. The following example demonstrates how to define three multi-assets where the third multi-asset depends on the second, and the second depends on the first:
python
from dagster import AssetIn, AssetOut, Output, multi_asset, AssetExecutionContext
# First multi-asset
@multi_asset(
outs={
"output1": AssetOut(is_required=False),
"output2": AssetOut(is_required=False),
},
can_subset=True,
)
def multi_asset_one(context: AssetExecutionContext):
if "output1" in context.op_execution_context.selected_output_names:
yield Output(value="value1_asset_one", output_name="output1")
if "output2" in context.op_execution_context.selected_output_names:
yield Output(value="value2_asset_one", output_name="output2")
# Second multi-asset, depending on the first multi-asset
@multi_asset(
ins={"input_from_asset_one": AssetIn(key="multi_asset_one")},
outs={
"output1": AssetOut(is_required=False),
"output2": AssetOut(is_required=False),
},
can_subset=True,
)
def multi_asset_two(context: AssetExecutionContext, input_from_asset_one):
if "output1" in context.op_execution_context.selected_output_names:
yield Output(value=f"processed_{input_from_asset_one}_asset_two", output_name="output1")
if "output2" in context.op_execution_context.selected_output_names:
yield Output(value=f"processed_{input_from_asset_one}_asset_two", output_name="output2")
# Third multi-asset, depending on the second multi-asset
@multi_asset(
ins={"input_from_asset_two": AssetIn(key="multi_asset_two")},
outs={
"output1": AssetOut(is_required=False),
"output2": AssetOut(is_required=False),
},
can_subset=True,
)
def multi_asset_three(context: AssetExecutionContext, input_from_asset_two):
if "output1" in context.op_execution_context.selected_output_names:
yield Output(value=f"processed_{input_from_asset_two}_asset_three", output_name="output1")
if "output2" in context.op_execution_context.selected_output_names:
yield Output(value=f"processed_{input_from_asset_two}_asset_three", output_name="output2")
In this updated example:
- multi_asset_one
remains unchanged and serves as the base asset.
- multi_asset_two
now has an input named input_from_asset_one
, which depends on an output from multi_asset_one
. The AssetIn
object is used to define this dependency, and the key
parameter specifies the asset key that multi_asset_two
depends on.
- multi_asset_three
has an input named input_from_asset_two
, which depends on an output from multi_asset_two
. Similarly, the AssetIn
object witScout
03/04/2024, 12:49 PMkey
parameter is used to define this dependency. When you execute these assets, Dagster will ensure that multi_asset_one
runs first, followed by multi_asset_two
, and finally multi_asset_three
, respecting the dependencies defined between them. The placeholder strings processed_{input_from_asset_one}_asset_two
and processed_{input_from_asset_two}_asset_three
are used to indicate that the output values of multi_asset_two
and multi_asset_three
are derived from their respective inputs. You should replace these placeholders with actual processing logic relevant to your use case.Peter Mburu
03/04/2024, 1:01 PMScout
03/04/2024, 1:01 PM["multi_asset_one"]
for asset ["output21"]
is not produced by any of the provided asset ops and is not one of the provided sources.
This error typically occurs when there is a mismatch between the asset keys used in the AssetIn
definitions and the actual asset keys that are produced by the multi-assets. In Dagster, asset keys are used to uniquely identify assets and to define dependencies between them.
To resolve this issue, you need to ensure that the asset keys specified in the AssetIn
definitions match the keys of the assets that are actually produced. Here are some steps to troubleshoot and fix the problem:
1. Check Asset Keys: Ensure that the asset keys you are using in the AssetIn
definitions match the keys of the assets produced by the corresponding multi-assets. Asset keys are usually derived from the names of the assets, but they can also be explicitly specified.
2. Define Asset Keys Explicitly: If you haven't already, you may want to define asset keys explicitly using the key
parameter of the AssetOut
class. This can help avoid any ambiguity about which assets are being referred to.
3. Review Asset Definitions: Go through your asset definitions to make sure that each asset is correctly producing the outputs that other assets depend on.
4. Check Repository Definition: Ensure that all assets are included in the repository definition and that there are no missing assets that could be causing the error.
Here's an example of how you might explicitly define asset keys to ensure that dependencies are correctly resolved:
```python
from dagster import AssetIn, AssetOut, Output, multi_asset, AssetExecutionContext
# First multi-asset
@multi_asset(
outs={
"output1": AssetOut(key="asset_one_output1", is_required=False),
"output2": AssetOut(key="asset_one_output2", is_required=False),
},
can_subset=True,
)
def multi_asset_one(context: AssetExecutionContext):
# ... (same as before)
# Second multi-asset, depending on the first multi-asset
@multi_asset(
ins={"input_from_asset_one": AssetIn(key="asset_one_output1")},
outs={
"output21": AssetOut(key="asset_two_output21", is_required=False),
"output22": AssetOut(key="asset_two_output22", is_required=False),
},
can_subset=True,
)
def multi_asset_two(context: AssetExecutionContext, input_from_asset_one):
# ... (same as before)
# Third multi-asset, depending on the second multi-asset
@multi_asset(
ins={"input_from_asset_two": AssetIn(key="asset_two_output21")},
outs={
"output31": AssetOut(keScout
03/04/2024, 1:02 PMAssetOut
is given an explicit key
, which is then used in the AssetIn
definitions to establish the dependencies. Make sure to replace the placeholder keys with the actual keys that make sense for your assets. If you continue to encounter issues, you may want to consult the Dagster documentation or seek help from the community for more specific guidance on defining and connecting multi-assets.Peter Mburu
03/04/2024, 2:52 PMScout
03/04/2024, 2:52 PMpython
from dagster import AssetIn, AssetOut, Output, multi_asset, AssetExecutionContext, AssetKey
# First multi-asset
@multi_asset(
outs={
"output1": AssetOut(key="multi_asset_one_output1", is_required=False),
"output2": AssetOut(key="multi_asset_one_output2", is_required=False),
},
can_subset=True,
)
def multi_asset_one(context: AssetExecutionContext):
if "output1" in context.op_execution_context.selected_output_names:
yield Output(value="value1_asset_one", output_name="output1")
if "output2" in context.op_execution_context.selected_output_names:
yield Output(value="value2_asset_one", output_name="output2")
# Second multi-asset, depending on the first multi-asset
@multi_asset(
ins={
"input_from_output1": AssetIn(key="multi_asset_one_output1"),
"input_from_output2": AssetIn(key="multi_asset_one_output2"),
},
outs={
"output21": AssetOut(key="multi_asset_two_output21", is_required=False),
"output22": AssetOut(key="multi_asset_two_output22", is_required=False),
},
can_subset=True,
)
def multi_asset_two(context: AssetExecutionContext, input_from_output1, input_from_output2):
if "output21" in context.op_execution_context.selected_output_names:
# Process the output of the first multi-asset
processed_value = f"processed_{input_from_output1}_asset_two"
yield Output(value=processed_value, output_name="output21")
if "output22" in context.op_execution_context.selected_output_names:
# Process the output of the second multi-asset
processed_value = f"processed_{input_from_output2}_asset_two"
yield Output(value=processed_value, output_name="output22")
Here are the changes made to the original code:
1. Explicit Asset Keys: Each AssetOut
in multi_asset_one
is given an explicit key using the key
parameter. These keys are used to identify the assets produced by multi_asset_one
.
2. Input Definitions: In multi_asset_two
, the ins
parameter is used to define the inputs that depend on the outputs of multi_asset_one
. The keys used in AssetIn
match the keys defined in the outputs of multi_asset_one
.
3. Removed internal_asset_deps
: The internal_asset_deps
parameter is not needed in this case because we are using the ins
parameter to define the dependencies. The internal_asset_deps
parameter is used when you need to specify dependencies between outputs within the same multi-asset.
4. *Passing InputsScout
03/04/2024, 2:53 PMinput_from_output1
and input_from_output2
are passed as arguments to multi_asset_two
and are used to process the values. 5. Processing Logic: The placeholders load the output of the first multi-asset
and load the output of the second multi-asset
have been replaced with actual processing logic that uses the inputs. With these changes, the dependencies between the multi-assets should be correctly defined, and the code should work as expected. Make sure to replace the processing logic with the actual logic you need for your use case.Peter Mburu
03/04/2024, 2:56 PMmulti_asset_two
Scout
03/04/2024, 2:56 PMmulti_asset_two
, you can use the AssetKey
to specify dependencies. However, you will need to ensure that the assets are part of the same job or repository so that Dagster can resolve the dependencies correctly.
Here's how you can define multi_asset_two
without passing inputs as arguments:
python
from dagster import AssetIn, AssetOut, Output, multi_asset, AssetExecutionContext, AssetKey
# First multi-asset
@multi_asset(
outs={
"output1": AssetOut(key="multi_asset_one_output1", is_required=False),
"output2": AssetOut(key="multi_asset_one_output2", is_required=False),
},
can_subset=True,
)
def multi_asset_one(context: AssetExecutionContext):
if "output1" in context.op_execution_context.selected_output_names:
yield Output(value="value1_asset_one", output_name="output1")
if "output2" in context.op_execution_context.selected_output_names:
yield Output(value="value2_asset_one", output_name="output2")
# Second multi-asset, depending on the first multi-asset
@multi_asset(
outs={
"output21": AssetOut(key="multi_asset_two_output21", is_required=False),
"output22": AssetOut(key="multi_asset_two_output22", is_required=False),
},
can_subset=True,
)
def multi_asset_two(context: AssetExecutionContext):
if "output21" in context.op_execution_context.selected_output_names:
# Assume we have a way to retrieve the value from the first multi-asset's output1
input_from_output1 = context.get_asset(AssetKey("multi_asset_one_output1"))
processed_value = f"processed_{input_from_output1}_asset_two"
yield Output(value=processed_value, output_name="output21")
if "output22" in context.op_execution_context.selected_output_names:
# Assume we have a way to retrieve the value from the first multi-asset's output2
input_from_output2 = context.get_asset(AssetKey("multi_asset_one_output2"))
processed_value = f"processed_{input_from_output2}_asset_two"
yield Output(value=processed_value, output_name="output22")
In this example, multi_asset_two
does not take inputs as arguments. Instead, it assumes that there is a way to retrieve the values of the first multi-asset's outputs within the execution context. The context.get_asset
method is a placeholder for the actual method you would use to retrieve the asset values. Dagster does not provide a context.get_asset
method, so you would need to implement the logic to retrieve the asset values based on your storage and execution environment.
Please note that the actual implementation of how you retrieve the asset values will depend on the specifics of your Dagster setup, such as the I/O maScout
03/04/2024, 2:56 PMmulti_asset_one
from the storage system directly, or use an I/O manager that can load the required asset values.Peter Mburu
03/04/2024, 3:30 PMinternal_asset_deps
argument for multi-asset 'multi_asset_two' on key 'output21'. Each specified asset key must be associated with an input to the asset or produced by this asset. Valid keys: [AssetKey(['output21']), AssetKey(['output22'])]
File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_core/errors.py", line 287, in user_code_error_boundary
yield
File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_grpc/server.py", line 242, in init
loadable_targets = get_loadable_targets(
^^^^^^^^^^^^^^^^^^^^^
File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_grpc/utils.py", line 50, in get_loadable_targets
else loadable_targets_from_python_module(module_name, working_directory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_core/workspace/autodiscovery.py", line 35, in loadable_targets_from_python_module
module = load_python_module(
^^^^^^^^^^^^^^^^^^^
File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_core/code_pointer.py", line 134, in load_python_module
return importlib.import_module(module_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/pmburu/miniconda3/lib/python3.11/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen importlib._bootstrap>", line 1204, in _gcd_import
File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 940, in exec_module
File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
File "/home/pmburu/repo/msc/uti/pipelines/__init__.py", line 8, in <module>
from .assets.antibiotic_resistance import multi_asset_one, multi_asset_two, multi_asset_three
File "/home/pmburu/repo/msc/uti/pipelines/assets/antibiotic_resistance.py", line 118, in <module>
@multi_asset(
^^^^^^^^^^^^
File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_core/definitions/decorators/asset_decorator.py", line 719, in inner
check.invariant(
File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_check/__init__.py", line 1612, in invariant
raise CheckError(f"Invariant failed. Description: {desc}")Scout
03/04/2024, 3:30 PMinternal_asset_deps
argument in the multi_asset_two
definition. Specifically, the error states that the asset keys specified in internal_asset_deps
are not valid because they must be associated with an input to the asset or produced by the asset itself.
In Dagster, internal_asset_deps
is used to specify dependencies between outputs within the same multi-asset. However, in your case, you are trying to specify dependencies on assets produced by a different multi-asset (multi_asset_one
). This is not the correct use of internal_asset_deps
.
To fix this issue, you should remove the internal_asset_deps
argument and instead use the ins
parameter to define inputs to multi_asset_two
that depend on the outputs of multi_asset_one
. However, since you mentioned that you do not want to pass inputs as arguments, you will need to ensure that the assets are part of the same job or repository so that Dagster can resolve the dependencies correctly.
Here's how you can define multi_asset_two
without using internal_asset_deps
and without passing inputs as arguments:
```python
from dagster import AssetOut, Output, multi_asset, AssetExecutionContext, AssetKey
# First multi-asset
@multi_asset(
outs={
"output1": AssetOut(key="multi_asset_one_output1", is_required=False),
"output2": AssetOut(key="multi_asset_one_output2", is_required=False),
},
can_subset=True,
)
def multi_asset_one(context: AssetExecutionContext):
if "output1" in context.op_execution_context.selected_output_names:
yield Output(value="value1_asset_one", output_name="output1")
if "output2" in context.op_execution_context.selected_output_names:
yield Output(value="value2_asset_one", output_name="output2")
# Second multi-asset, depending on the first multi-asset
@multi_asset(
outs={
"output21": AssetOut(key="multi_asset_two_output21", is_required=False),
"output22": AssetOut(key="multi_asset_two_output22", is_required=False),
},
can_subset=True,
)
def multi_asset_two(context: AssetExecutionContext):
if "output21" in context.op_execution_context.selected_output_names:
# Logic to retrieve the value from the first multi-asset's output1
# This logic will depend on your specific setup and how you manage asset storage
# For example, you might retrieve the value from a database or a file system
output1_value = "retrieved_value1" # Placeholder for the actual retrieval logic
yield Output(value=f"processed_{output1_value}_asset_two", output_name="output21")
if "output22" in context.op_execution_context.selected_output_names:
# Logic to retrieve the value from the first multi-asset's output2
#Scout
03/04/2024, 3:31 PMmulti_asset_two
does not use internal_asset_deps
and does not take inputs as arguments. Instead, it assumes that there is a way to retrieve the values of the first multi-asset's outputs within the execution context. The actual implementation of how you retrieve the asset values will depend on the specifics of your Dagster setup, such as the I/O manager you are using and how your assets are stored. You may need to access the outputs of multi_asset_one
from the storage system directly, or use an I/O manager that can load the required asset values.Peter Mburu
03/04/2024, 5:48 PMScout
03/04/2024, 5:48 PMoutput_for_node
method is being called with an output name ("result"
) that does not exist for the node "remote_imf_data"
. The output_for_node
method is used to retrieve the output of a specific node (in this case, an asset) from the result of a pipeline run or asset materialization. Here's the relevant part of the error message: dagster._core.errors.DagsterInvariantViolationError: remote_imf_data has no output named result.
This error suggests that within the remote_imf_data
asset, there is no output defined with the name "result"
. To resolve this issue, you should: 1. Check the definition of the remote_imf_data
asset to see what outputs it actually defines. Ensure that you are using the correct output name when calling output_for_node
. 2. If you expect the remote_imf_data
asset to have an output named "result"
, you need to make sure that the asset is yielding an Output
with output_name="result"
. 3. If the remote_imf_data
asset is supposed to yield multiple outputs, ensure that each Output
is yielded with the correct output_name
that matches what you are trying to retrieve with output_for_node
. 4. If the remote_imf_data
asset only yields a single output and does not specify an output_name
, you can retrieve the output without specifying an output name, as Dagster will use the default output name. Here's an example of how you might modify the remote_imf_data
asset to yield an output named `"result"`: python from dagster import Output, asset @asset def remote_imf_data(): # ... your logic here ... yield Output(value=some_value, output_name="result")
And here's how you would retrieve the output in your test: python output_asset2 = result.output_for_node("remote_imf_data", "result")
Make sure that the output name you use in output_for_node
matches the output_name
specified when yielding the Output
in your asset definition. If the asset does not specify an output name, you can omit the output name in output_for_node
, and it will default to the standard output name.Peter Mburu
03/04/2024, 6:22 PMScout
03/04/2024, 6:22 PM@multi_asset
decorator, you specify multiple outputs, each potentially corresponding to a different asset. When you materialize or execute the multi-asset, you can access the individual assets by their output names. Here's a simplified example of how you might define a multi-asset and then access one of its assets: python from dagster import AssetOut, Output, multi_asset @multi_asset( outs={ "asset1": AssetOut(), "asset2": AssetOut(), } ) def my_multi_asset(): # Your logic to produce values for asset1 and asset2 yield Output(value="value_for_asset1", output_name="asset1") yield Output(value="value_for_asset2", output_name="asset2") # Later, when you materialize or execute the multi-asset, you can access the individual assets: result = materialize([my_multi_asset]) value_for_asset1 = result.output_for_node("my_multi_asset", "asset1") value_for_asset2 = result.output_for_node("my_multi_asset", "asset2")
In this example, my_multi_asset
is a multi-asset that yields two outputs, each corresponding to a different asset. When you call materialize([my_multi_asset])
, both assets are materialized, and you can access the output of each asset using the output_for_node
method on the result object, specifying the multi-asset's name and the output name. If you only want to materialize a subset of the assets defined in a multi-asset, you can use the AssetSelection
API to specify which assets you want to materialize. Here's an example of how you might do that: python from dagster import AssetSelection, materialize # Assuming 'my_multi_asset' is defined as above # Materialize only 'asset1' from the multi-asset result = materialize( AssetSelection.assets(my_multi_asset).with_output("asset1") ) value_for_asset1 = result.output_for_node("my_multi_asset", "asset1")
In this example, AssetSelection.assets(my_multi_asset).with_output("asset1")
specifies that only the output named "asset1"
from the multi-asset my_multi_asset
should be materialized. Remember that the actual implementation of accessing individual assets from a multi-asset will depend on your specific use case and the way you have set up your Dagster assets and materialization process.Peter Mburu
03/04/2024, 6:30 PMScout
03/04/2024, 6:30 PMwith_output
on an object of type KeysAssetSelection
, which does not have this method. The AssetSelection
API in Dagster is used to select specific assets for materialization, but the way it's being used in the code snippet from the error message seems to be incorrect. As of my knowledge cutoff date, Dagster does not have a with_output
method on the AssetSelection
class. Instead, you should use the AssetSelection
API to select assets by their keys. If you want to materialize a specific asset or a subset of assets, you can use the AssetSelection
to specify which assets to include. Here's an example of how you might correctly use AssetSelection
to select a specific asset for materialization: python from dagster import AssetSelection, materialize # Assuming 'remote_imf_data' is a multi-asset with an output asset named 'gpd_remote_data' # Select the specific asset you want to materialize selected_assets = AssetSelection.keys("gpd_remote_data") # Materialize the selected asset result = materialize(selected_assets)
In this example, AssetSelection.keys("gpd_remote_data")
is used to create a selection of assets by their keys. You would replace "gpd_remote_data"
with the actual key of the asset you want to materialize. If you're trying to materialize a specific output from a multi-asset, you would typically not need to use AssetSelection
for that purpose. Instead, you would materialize the multi-asset and then access the specific output you're interested in from the result. If you continue to encounter issues, please ensure that you are using the latest version of Dagster and refer to the most recent documentation for any updates or changes to the API. If the issue persists, you may need to seek further assistance from the Dagster community or support channels.Peter Mburu
03/04/2024, 6:48 PMScout
03/04/2024, 6:48 PMmaterialize
function is expecting a sequence of AssetsDefinition
or SourceAsset
objects, but it received an object of type KeysAssetSelection
instead. The materialize
function is used to materialize assets, and it requires a list of asset definitions or source assets as its argument. The KeysAssetSelection
object is part of the AssetSelection
API, which is used to select specific assets by their keys. However, it seems that the KeysAssetSelection
object is being passed directly to the materialize
function, which is not the correct usage. To resolve this issue, you need to pass the actual asset definitions that you want to materialize to the materialize
function. If you have a list of asset keys and want to materialize those specific assets, you need to ensure that you are passing the corresponding asset definitions. Here's an example of how you might correctly use the materialize
function with asset definitions: python from dagster import materialize # Assuming you have asset definitions like the following: # asset1, asset2, ... # You would pass the asset definitions directly to the materialize function result = materialize([asset1, asset2], partition_key="2022-01-01")
In this example, asset1
, asset2
, etc., are the asset definitions that you want to materialize. You would replace these with the actual asset definitions that correspond to the asset keys you are interested in. If you are trying to materialize assets based on a selection of keys, you need to convert those keys into the corresponding asset definitions before calling materialize
. The AssetSelection
API can be used to filter assets within a repository or job, but the materialize
function requires the actual asset definitions. Please ensure that you are using the latest version of Dagster and refer to the most recent documentation for any updates or changes to the API. If the issue persists, you may need to seek further assistance from the Dagster community or support channels.Peter Mburu
03/04/2024, 6:59 PMScout
03/04/2024, 6:59 PMKeysAssetSelection
, you would typically be working within the context of a Dagster repository where your assets are defined. The KeysAssetSelection
object is used to create a selection of assets based on their keys, but to materialize those assets or to work with their definitions, you need to resolve them to the actual AssetsDefinition
objects within your repository. As of my knowledge cutoff date, the documentation does not provide a direct method to convert a KeysAssetSelection
into a list of AssetsDefinition
objects for use with the materialize
function. However, if you have access to the repository definition, you may be able to retrieve the AssetsDefinition
objects by their keys. Here's a conceptual example of how you might retrieve AssetsDefinition
objects from a repository using the asset keys: python from dagster import AssetKey, RepositoryDefinition # Assuming you have a repository definition repository: RepositoryDefinition = ... # And you have a KeysAssetSelection with some asset keys keys_asset_selection = AssetSelection.keys(AssetKey("my_asset1"), AssetKey("my_asset2")) # You would retrieve the actual asset definitions from the repository using the asset keys assets_defs = [ repository.get_asset_def(asset_key) for asset_key in keys_asset_selection.selected_keys ] # Now you can use the assets_defs with the materialize function result = materialize(assets_defs, partition_key="2022-01-01")
In this example, repository.get_asset_def(asset_key)
is a placeholder for the method you would use to retrieve the AssetsDefinition
object for a given AssetKey
from your repository. You would need to replace this with the actual method provided by your repository or the context in which you are working. Please note that the actual implementation may vary based on the version of Dagster you are using and the structure of your repository. If you are unable to find a method to retrieve AssetsDefinition
objects by their keys, you may need to manually define a list of the assets you want to materialize based on your knowledge of the repository's structure. If you need further assistance, please refer to the latest Dagster documentation or seek help from the Dagster community.Peter Mburu
03/04/2024, 7:02 PMScout
03/04/2024, 7:02 PMAssetSelection
object directly in the list of assets to be materialized. However, the materialize
function expects a list of asset definitions, not an AssetSelection
object. To fix the code, you need to ensure that you are passing a list of asset definitions to the materialize
function.
Here's a revised version of the test function:
python
from dagster import materialize, AssetKey
def test_import_imf_data(self):
with conditional_logging():
with patch(
"dagster_pipelines.assets.ingestion.price.imf_ingestion.fetch_remote_imf_data"
) as mock_process, patch(
"dagster_pipelines.assets.ingestion.price.imf_ingestion.get_data_source_documents_for_web_service"
) as mock_filter, patch(
"dagster_pipelines.assets.ingestion.price.imf_ingestion.fetch_imf_metadata"
) as mock_metadata:
mock_process.return_value.ok = True
mock_process.return_value = self.mock_data
mock_filter.return_value.ok = True
mock_filter.return_value = self.datasourcedocument
mock_metadata.return_value.ok = True
mock_metadata.return_value = self.mock_metadata_df
# Assuming get_imf_datasource_documents, get_imf_data_series, and imf_metadata
# are asset definitions, and remote_imf_data is a multi_asset definition.
# You need to pass the asset definitions directly to the materialize function.
# If you want to materialize a specific asset from a multi_asset, you need to
# get the asset definition for that specific asset.
# Assuming 'gpd_remote_data' is the key for the asset you want to materialize
# from the multi_asset 'remote_imf_data', you would retrieve the asset definition
# from your repository. The following line is a placeholder for the actual retrieval:
gpd_remote_data_asset = repository.get_asset_def(AssetKey("gpd_remote_data"))
# Now you can include the asset definition in the list of assets to materialize
assets = [
get_imf_datasource_documents,
get_imf_data_series,
imf_metadata,
gpd_remote_data_asset # This should be the actual asset definition
]
# Materialize the assets
result = materialize(assets, partition_key="2022-01-01")
assert result.success
In this revised code:
- We assume that get_imf_datasource_documents
, get_imf_data_series
, and imf_metadata
are asset definitions that you want to materialize.
- We replace the AssetSelection
object with the actual asset dScout
03/04/2024, 7:03 PM"gpd_remote_data"
. You need to retrieve this asset definition from your repository, which typically involves accessing the repository object and calling a method like get_asset_def
with the AssetKey
. - The placeholder repository.get_asset_def(AssetKey("gpd_remote_data"))
should be replaced with the actual code to retrieve the asset definition from your repository. Please note that the actual method to retrieve an asset definition from a repository may vary based on your specific setup and the version of Dagster you are using. If you need further assistance, please refer to the latest Dagster documentation or seek help from the Dagster community.